Optimizing an App-Based Just-in-Time Adaptive Intervention for Stimulant Use Among Sexual Minority Men Living with HIV: Protocol for a Community-Engaged Research Approach and Hybrid-Experimental Design
Bibliographic record
Abstract
BACKGROUND: In the United States, sexual minority men (SMM) are disproportionately affected by HIV. For this population, intersecting sexual minority- and HIV-related stressors add to general life stress, increasing health risks. Stress is associated with HIV progression and is linked to transmission risk behaviors, such as medication nonadherence and substance use. Substance use is a particularly important risk factor for HIV transmission; beyond injection drug use, recreational use is associated with sexual transmission risk behaviors and an increased risk of HIV among SMM. Interventions targeting stress responses may be especially useful for HIV risk reduction among substance-using SMM (SUSMM) living with HIV (LWH). Positive affect interventions have shown promise in reducing stress in the context of chronic illness, including HIV. However, few studies have examined these interventions and their potential health benefits for SUSMM-LWH. OBJECTIVE: This protocol describes a project (R34DA053999) that builds on our pilot work exploring the induction of positive affect as a potential intervention to reduce stress and improve health outcomes among SMM-LWH. This protocol aims to employ a community-engaged research approach, using the multiphase optimization strategy, to iteratively tailor our app-based positive affect intervention, TeaTime, for SUSMM-LWH. METHODS: In phase 1, we enrolled 10 SUSMM-LWH into an open-phase pilot using our existing app-based ecological momentary intervention (EMI), which employs a just-in-time adaptive intervention design. We collaborated with a community advisory board to tailor our intervention content and app design using focus group feedback. In phase 2, we conducted a pilot factorial optimization trial to assess the acceptability and feasibility of adding 2 new features that may enhance intervention design and delivery. Specifically, we piloted a 2×2 factorial design, randomizing 80 SUSMM-LWH to receive either (1) random craving prompts or (2) smartwatch integration or both or neither. Analyses will examine group differences on feasibility and acceptability outcomes, as measured by the System Usability Scale and Mobile App Rating Scale, as well as retention and engagement. RESULTS: The project was funded in February 2022, and data collection for phase 1 was completed in December 2023. Phase 2 was launched in July 2024, with data collection completed by August 2025. Results from phase 1 are expected to be submitted for publication by December 2025. CONCLUSIONS: In this protocol, we combine methods from social science, intervention science, and software development into a single innovative approach to enhance and optimize an app-based, just-in-time adaptive, EMI for SUSMM-LWH. Findings from this study will support the development of a fully optimized intervention ready for evaluation and implementation. In a future randomized controlled trial, the ecological momentary assessment design will also enable the collection of day-level data on intervention efficacy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/76741.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".